Gabora, L. (2007). Self-other organization: Why early life did not evolve through natural selection.
Journal of Theoretical Biology, 241(3), 443-450.

Self-Other
Organization:

Why Early
Life did not Evolve through Natural Selection

Liane
Gabora

University of
British Columbia

Address
for correspondence:

Liane Gabora

Psychology and Computer
Science

University of British
Columbia

Okanagan Campus, 3333
University Way

Kelowna, BC V1V 1V7,
CANADA

Email:
liane.gabora[at]ubc.ca

Phone: (250)
807-9849

Abstract: The
improbability of a spontaneously generated self-assembling molecule has
suggested that life began with a set of simpler, collectively replicating
elements, such as an enclosed autocatalytic set of polymers (or
autocell). Since replication occurs without a self-assembly
code, acquired characteristics are inherited. Moreover, there is no strict
distinction between alive and dead; one can only infer that an autocell
was alive if it replicates. These features of early life
render natural selection inapplicable to the description of its change-of-state
because they defy its underlying assumptions. Moreover, natural selection
describes only randomly generated novelty; it cannot describe the emergence of
form at the interface between organism and environment. Self-organization is
also inadequate because it is restricted to interactions amongst parts; it too
cannot account for context-driven change. A modified version of selection theory
or self-organization would not work because the description of change-of-state
through interaction with an incompletely specified context has a completely
different mathematical structure, i.e. entails a
non-Kolmogorovian probability model. It is proposed that the evolution of early
life is appropriately described as lineage transformation through context-driven
actualization of potential, with self-organized change-of-state being a special
case of no contextual influence, and competitive exclusion of less fit
individuals through a selection-like process possibly (but not necessarily)
playing a secondary role. It is argued that natural selection played an
important role in evolution only after genetically mediated replication was
established.

This paper calls into question the assumption that early life
evolved, like present-day life, through natural selection, and is rightly
thought of and formally described in terms of ‘units of selection’. Since Darwin
(1859) proposed the concept of natural selection, it has been thought that, in
order to be applicable to the evolution of an entity, certain conditions must be
met, such as (1) heritable random variation and (2) competition for scarce
resources. Clearly life must exist and have replicated at least once before
there can be variation and competition. So it is almost trivially true that
natural selection cannot explain how the first (or second) animate entity came
about. However, it is often assumed that once there were two or more, any
ensuing adaptive change was due to natural selection. We will see that according
to reasonable and widely accepted scenarios for how life began even this is
unlikely, because natural selection further (3) prohibits inheritance of
acquired characteristics (or at least requires that it be negligible compared to
change due to differential replication of individuals with heritable variation
competing for scarce resources). Moreover, even self-organization cannot
describe the change-of-state that ensues when this condition is not met, because
it is restricted to interactions amongst the parts of an entity; it cannot
account for form that emerges at the interface between entity and
environment.

The prohibition on inheritance of acquired characteristics is
straightforward for present-day life, but not so for early life. The problem
stems from the unlikelihood that early life replicated in a manner similar to
present-day life, owing to the extreme improbability of a self-assembling
molecule arising spontaneously. Recognition of this led to the well-known
‘chicken and egg’ problem: which came first, the nucleotides that make up a
genetic self-assembly code which through transcription and translation leads to
proteins, or the proteins that are necessary to many stages of the
transcription/translation process?

Many attempts to reconcile this paradox have been put forth
(for an overview, see Fry 2000). Metabolism-first theories propose that the
reducing atmosphere of early earth was conducive to the formation of a soup of
organic molecules from amongst which the precursors of life arose (Haldane,
1929; Oparin, 1929/1936, 1957). Demonstration that amino acids could form
spontaneously by an electrical dischange under conditions thought to simulate
earth’s early atmosphere (Miller, 1953) led to a suggestion that life began with
weakly catalytic proteins (Fox, 1988, 1993). Replication-first theories propose
that life began with information carrying nucleotides or template-replicating
polymers that together constitute a primitive genetic code (e.g. Crick, 1968; Gilbert, 1986; Lifeson, 1997; Lifson
and Lifson, 1999; Orgel, 1992, 1995; Maynard-Smith and Szathmáry, 1999). Other
approaches can be considered a blend of metabolism-first and replication-first
approaches (de Duve, 1995; Dyson, 1982, 1999; Eigen, 1971, 1992).

Still other theories, which may be referred to as
‘self-organization’ theories, suggest that life began with a chemically
isolated, self-organized set of simple collectively replicating polymers, and genetically mediated
replication came afterward (Deacon, submitted; Kauffman, 1986, 1993, 1995, 2000;
Morowitz, 1992; Wäechtershäeuser, 1992; Weber, 1998, 2000; Williams and Frausto
da Silva, 1999, 2002, 2003). It is this theory of the origin of life that is
presupposed in what follows. For simplicity, the process by which one organism
generates another will be referred to as coded replication when it isgenetically mediated, and uncoded
replication when it is not[1].
Following Deacon, enclosed life forms that replicate through an uncoded,
self-organized process are referred to as autocells.

We begin by examining how the assumptions underlying natural
selection are incompatible with characteristics of autocells. We will look at a
means of conceptualizing the evolution of early life that is consistent with
reasonable scenarios for how life began and current theories of change-of-state.
Specifically, it will be proposed that early life evolved through a process of
self-other organization¾the
emergence of form not just internal to an entity but at the interface between
entity and context¾which
is described as an interleaving of self-organization and context-driven
actualization of potential, or CAP (Gabora and Aerts, 2005a).

Analysis of the formal requirements for self-replication led
von Neumann (1966) to conclude that a self-replicating automaton (sometimes
referred to as a replicator) consists of coded information that gets used in two
distinct ways. The first way is as a set of self-assembly instructions that are
actively deciphered to construct a replicant. In this case, the code functions
as interpreted information. The second way is as a self-description that is
passively copied to the next replicant. In this case, the code functions as
uninterpreted information. There are of course deviations from this in
present-day life, viruses being an oft-cited example. However these deviations
can be accommodated within a Darwinian framework.

Investigations into the question of how life began have quite
naturally focused largely on the question of how such self-assembly instructions
could have arisen spontaneously in the atmosphere of early earth. However,
investigations of the possible relevance of connectivity and threshold phenomena
in random graphs (Cohen, 1988; Erdös and Rényi, 1960) suggested an alternative
possibility: that life arose without explicit template replication through
autocatalytic closure of simple catalytic molecules (Bollobas, 2001; Bollobas
and Rasmussen, 1989; Dyson, 1982, 1985; Kauffman 1986, 1993). In Kauffman’s
model, polymers catalyze reactions that generate other polymers, increasing
their joint complexity, until together as a whole they form something that can
more or less replicate itself.[2]
The reason this works is that when polymers interact, the number of different
polymers increases exponentially, but the number of reactions by which they can
interconvert increases faster than their total number. Thus, as their diversity
increases, so does the probability that some subset of the total reaches a
critical threshold where there is a catalytic pathway to every member. The set
is autocatalytically closed because
although no polymer catalyzes its own replication, each catalyzes the
replication of another member of the set. So long as each polymer is getting
duplicated somewhere in the set, eventually multiple copies of all polymers
exist.

At least some subset of the polymers spontaneously adhere to
one another, forming a spherical vesicle such as a coascervate (Oparin, 1957),
microsome (Fox and Dose, 1977; Fox, 1988), or liposome (Hargreaves et al. 1977;
Deamer and Barchfeld, 1982) that encloses the polymer set (see also Deacon,
submitted). Such a structure is prone to fission or budding, where part of the
vesicle pinches off and it divides in two. Replication is far from perfect, thus
‘offspring’ are unlikely to be identical to ‘parent’. But so long as there is at
least one copy of each polymer in each of the two resulting vesicles, they can
self-replicate, and continue to do so indefinitely, or until their structure
changes drastically enough that self-replication capacity breaks down.

Thus we have two kinds of replicators (Gabora, 2004). Coded replicators such as present-day organisms use
self-assembly instructions as proposed by von Neumann. This ensures they
replicate with high fidelity, and acquired characteristics are
not inherited.
Replication-first theories assume that the earliest forms of life were of this
kind. The second, uncoded replicators employ a self-organized autocatalytic process.
Some form of uncoded replication is assumed by theories of the origin of life
that do not start with template-replicating polymers, such as the autocatalytic
model discussed here.

A criticism of Kauffman’s origin of life scenario is that it
lacks a means of generating heritable variation, and thus a mechanism for
evolution (Lifson, 1997; Maynard-Smith and Szathmary, 1995). But as we have
seen, because there is nothing to prohibit inheritance of acquired
characteristics, the ‘usual’ impediments to the generation of novel variation do
not exist. Since autocell replication is accomplished through an autocatalytic
process rather than decoding of a genetic template,change accumulated over the course of a
lifetime is not wiped out at the end of each generation but drawn into the
lineage. Different chance encounters of polymers, or differences in their
relative concentrations, or the appearance of new polymers, could all result in
different polymers catalyzing a given reaction, which in turn alters the set of
reactions to be catalyzed. The accumulation of acquired change can be to the
extent that by the time the autocell divides it may already have changed
dramatically. Thus the ‘replicant’ daughter cell can have quite a different
dynamical structure from that of the parent autocell at the time of its own
conception. This is sometimes referred to as Lamarckian evolution, though strict
interpretations of the concept additionally require that the traits in question
be acquired at the phenotypic level and subsequently modify the genotype to
become heritable (e.g. Hull et
al. 2001)[3].
This is clearly not the case here, for there is no clear genotype/phenotype distinction.
Specifically, since replication is not template-driven, there is no portion of
the autocell that is (like the genotype) shielded from contextually driven
(environmentally accrued) change. And there is no portion of the autocell that
is (like the phenotype) shed at the end of the generation. A change to one
polymer or replacement of one by another would be retained in at least one of
the two daughter cells after fission, and this could cause other changes that
have a significant effect on the lineage further downstream. It was not until
the advent of explicit self-assembly instructions that acquired characteristics
were no longer passed on to the next generation. From that point on, the only
contextual interactions that exert much of an effect are those that affect the
generation of offspring.

Kauffman suggests that the uncoded-to-coded transition began
with the chance evolution of polymers with a tendency to attach small molecules
such as amino acids to their surfaces. Given their exterior location, these
amino acids interact more with the environment than the polymers housed inside.
This set the stage for a division of labor between the proteins that interact
with the environment, and the nucleic acids concerned with replication. As Weber
(2000) notes, any chemical change that increases capacity to
remember information that enhances
autocatalytic activity by encoding it in the polymers of nucleic acids,
constitutes a step toward what we now think of as the genetic code. This is of
course significant, in part because it enables informative acts to be carried
out recursively, hierarchically, and with greater precision, causing the
replication process to become more constrained, robust, and shielded from
external influence. From this point onward, acquired change only affected the
lineage if it impacted the generation and survival of progeny (such as by
affecting the capacity to attract mates, or engage in parental care).

In present-day life, the prohibition on inheritance of
acquired characteristics means that change accrued over a lifetime of an
individual is not drawn into that individual’s lineage but obliterated at the
end of each generation. Thus it doesn’t enter into the long-term picture, and we
can afford to ignore it. Therefore, while for present-day life it is possible to
describe the evolution process without a means of describing form that
actualizes at the interface between entity and context, for autocells this is
not the case. Acquired change occurs through the sculpting of existing structure
rather than through selection of competing alternative structures. In other
words, autocells get transformed and
need not get selected amongst.
Thus change is often occurring not through selection amongst individuals in a
population of
physically realized entities but
through potential variations of a
single entity.

Selection theory is inappropriate for the description of this
kind of change of state. It can describe variation in a population of real
organisms, but not potential variations of a single organism. More generally,
when the entity of interest is a set of multiplephysically realized or actual (as opposed to
potential) sub-entities from amongst which some subset is
selected, this is a deterministic
process which can be described by a classical probability theory such as
selection theory. Selection theory is concerned only with factors internal to
the population, so nondeterminism in our model reflects a lack of knowledge of
the state of the entity (the entity being not any one particular organism but
the population). Nondeterminism that arises through lack of knowledge concerning
the state of the entity can be described by classical stochastic models
(e.g. Markov processes) because
the probability structure is Kolmogorovian[4].

However, when change occurs not through selection amongst
alternatives but through actualization of potential through interaction with a
context, nondeterminism arises through lack of knowledge concerning the
interaction between the entity and the context. It has been proven that this
introduces a non-Kolmogorovian probability model on the state space, thus Bayes’
formula for conditional probability is not satisfied (Pitowski, 1989). A
Kolmogorovian probability model such as is used in population
genetics, cannot be used (Accardi and Fedullo, 1982; Aerts, 1986; Aerts
and Aerts, 1997; Piron, 1976; Randall and Foulis, 1976). Because the entity has
the potential to change to many different states (given the various possible
contexts it could encounter), we can say that it is in a potentiality
state with respect to context. The
mathematical description of change-of-state of an entity that is in a state of
potentiality (where, given different contexts it would achieve different forms)
is considerably more difficult than that of a population of already actualized
(physically existing) entities whose change-of-state is taking place through
natural selection. A fundamentally different kind of mathematical structure is
required. It is only possible to ignore the problem of
incomplete knowledge of context if all contexts are equally likely, or if
context has a temporary or limited effect.

Potentiality and contextuality both stem from the fact that
we inevitably have incomplete knowledge of the universe in which an entity is
operating. When the state of the entity of interest and/or context are in
constant flux, or undergoing change at a resolution below that which we can
detect but nevertheless affect what emerges at the entity-context interface,
this gives rise in a natural way to nondeterministic change. In reality the
universe is so complex we can never describe with complete certainty and
accuracy the context to which an entity is exposed, and how it interacts with
the entity. There is always some possibility of even very unlikely outcomes.
However, there are situations in which we can predict the values of relevant
variables with sufficient accuracy that we may consider the entity to be in a
particular state, and other situations in which there is enough uncertainty to
necessitate the concept of potentiality. Thus a model of the evolution of an
entity must ordinarily take into account the degree of knowledge we as observers have about the context. But because
in the modern-day evolution of biological form acquired traits are not
heritable, we can get away with a model that not incorporate the effect of
context.

Another assumption of selection theory is that individuals
are lost from the population and replaced by new ones, giving rise to discreet
or overlapping generations. The notion of generation is sometimes construed in
more abstract treatments as iteration
(e.g. Holland, 1975; Hull
et al., 2001). Mathematical
analyses of selection processes are predicated on the ability to state up front
whether particular individuals do or do not constitute members of a given
generation or iteration.

However, ‘death’ of an autocell goes unnoticed; concretely
there is not much to distinguish a ‘dead’ autocell from a ‘living’ one except
one continues to spawn replicants and the other doesn’t. Indeed we must wait
until after the fact of its replication to determine that it was ‘alive’ prior
to the replication event. A seemingly dead autocell could ‘come back to life’
when the circumstances for its replication became right. Because there is no
hard and fast distinction between a living individual and a dead one, there is
no definitive basis for determining what constitutes a generation.

The success of self-organized autocatalysis as an explanation
for the origin of life might appear to suggest that self-organization can take
over where natural selection leaves off and together they provide a complete
explanation of the origin and evolution of living things. Indeed, to the extent
that the state of an autocell at a particular point in time reflects its
composition of polymers and their internal pattern of catalysis, its dynamics
can be described by recourse to self-organization. However, to the extent that
the autocell’s state reflects interaction that takes place between it and its environment,
natural selection and self-organization are both insufficient. Self-organization
can explain how parts reorganize to give rise to an entity that may have
properties that were not present in the parts. But it is still limited to the
entity and its parts. It cannot describe change that occurs due to the
potentiality to actualize new form through interaction with different contexts
that could be encountered.

In this section we review a general scheme for change of
state of an entity under the influence of a context (Gabora and Aerts, 2005a).
In the section that follows we will examine its implications for the origin of
life.

Since we do not always have perfect knowledge of the state of
the entity, the context, and the interaction between them, a general description
of an evolutionary process must be able to cope with nondeterminism.
Evolutionary systems differ with respect to the degree of determinism involved
in the changes of state that the entity undergoes. Consider an entity in a state
p(ti) at an instant of time
ti. If it is under the
influence of a context e(ti), and we know with certainty that
p(ti) changes to state
p(ti+1) at time
ti+1, we refer to the
change of state as deterministic.
Newtonian physics provides the classic example of deterministic change of state.
Knowing the speed and position of a ball, one can predict its speed and position
at some time in the future. In many situations, however, an entity in a state
p(ti) at time
ti under the influence
of a context e(ti) may
change to any state in the set
{p1(ti+1),
p2(ti+1),
…, pn(ti+1), … }. When more than one change of state is
possible, the process is nondeterministic.

Nondeterministic change can be divided into two kinds. In the
first, the nondeterminism originates from a lack of knowledge concerning the
state of the entity p(ti)
itself. This means that deep down the change is deterministic, but since we lack
knowledge about what happens at this deeper level, and since we want to make a
model of what we know, the model we make is nondeterministic. This kind of
nondeterminism is modeled by a stochastic theory that makes use of a probability
structure that satisfies Kolmogorov’s axioms.

Another possibility is that nondeterminism arises through
lack of knowledge concerning the context e(ti), or how the context interacts with the entity of interest[5].
It must be stressed that a potentiality state is not
predetermined, just waiting for
its time to come along, at least not insofar as our models can discern, possibly
because we cannot precisely specify the context that will come along and
actualize it. Note also that a state is only a potentiality state in
relation to a certain (incompletely
specified) context. It is possible for a state to be a potentiality state with
respect to one context, and a deterministic state with respect to another. More
precisely, a state that is deterministic with respect to a context can be
considered a limit case of a potentiality state, with zero
potentiality.

We have seen that the description of the evolutionary
trajectory of an entity may involve nondeterminism with respect to the state of
the entity, the context, or how they interact. An important step toward the
development of a complete theory of evolution is to find a mathematical
structure that can incorporate all these possibilities. There exists an
elaborate mathematical framework for describing the change and actualization of
potentiality through contextual interaction that was developed for quantum
mechanics. However it has several limitations, including the linearity of the
Hilbert space, and the fact that one can only describe the extreme case where
change of state is maximally contextual.
Other mathematical theories lift the quantum formalism out of its specific
structural limitations, making it possible to describe nondeterministic effects
of context in other domains (Aerts 1993; Aerts and Durt 1994; Foulis and Randall
1981; Foulis et al. 1983; Jauch
1968; Mackey 1963; Piron 1976, 1989, 1990; Pitowsky 1989; Randall and Foulis
1976, 1978). The original motivation for these generalized formalisms was
theoretical (as opposed to the need to describe the reality revealed by
experiments). With these formalisms it is possible to describe macro-level as
well as micro-level situations with any degree of contextuality (Aerts, 1982,
1991; Aerts et al., 2000). In fact, classical and quantum come out as special
cases: quantum at one extreme of complete contextuality, and classical at the
other extreme, complete lack of contextuality (Aerts, 1983; Piron, 1976). These
formalisms can be applied to evolution processes, which differ according to not just the degree
of contextuality but also
according to the degree of internalization of and dependency upon context,
as well as whether nondeterminism, if present, is due to lack of knowledge
concerning the state of the entity or lack of knowledge concerning the state of
the context (Gabora and Aerts, 2005a).

Both coded and uncoded replicators evolve by actualizing
potential that exists due to the state of the entity, the context, and the
nature of their interaction. For modern-day life, context-driven change at the
level of the individual is lost from the lineage, so natural selection, a less
direct, population-level means of change, becomes significant. But because
autocell replication occurs not according to instructions, but through
happenstance interactions, context-driven change is retained. There is no a
priori reason such a process has to be
Darwinian or involve selection. To the extent that the different possible
contexts an autocell could encounter would give rise to a different interaction
dynamics, the description of this change of state requires a nonclassical
probability model. We may refer to emergence of form that results through this
interaction between self and context as self-other
organization to distinguish it from form
that emerges through interaction amongst the parts of an entity. Self-other
organization is not a competing explanation to self-organization. They work in
concert; novelty generated through self-other organization at the interface
between entity and context propagates through the entity to bring about
self-organized change from within.

Let us now consider a scheme for describing context-driven
evolution¾change
of state of an autocell. Let us say an autocell undergoes a change of state from
p0(t0) to
p4(t1). The
change of state of the autocell may evoke a change in its environment or
context. Alternatively, the context may change of its own accord, or the ensuing
self-organization may change the sort of context it is subsequently susceptible
to. Under the influence of this (possibly altered) context, which we call
e(t1), there may be
many potential states it could change to. We denote this set of states
{p1(t2),
p2(t2), …,
pn(t2), …
}. At time t2, one of
these states, for example p3(t2), may actualize. And so forth, recursively. The states
p(t0),
p(t1),
p(t2), …,
p(ti), … constitute
the trajectory of the autocell through state space, and describe its evolution
in time. Thus, the evolution of an autocell is described as incremental change
resulting from recursive, context-driven actualization of
potential, or CAP.This process may consist of both
deterministic and nondeterministic segments. In deterministic
segments, the autocell changes state in a
way that follows predictably given its previous state and/or the context to
which it is exposed, and this can be described as a process of
self-organization. In nondeterministic segments this is not the case; self-other organization is
needed.

Clearly, the transition from uncoded to coded replication,
while ensuring fidelity of replication, decreased long-term sensitivity to and
internalization of context, and thus capacity for context independence.
Thereafter until the advent of sexual replication, one generation was almost
identical to the next, the evolution process became more deterministic. With the
advent of sexual reproduction, the contextuality of biological evolution
increased. Consider an organism that is heterozygous for trait X with two
alleles A and a. The potential of this Aa organism gets actualized differently depending on
the context provided by the genotype of the organism’s mate. In the context of
an AA mate, the
Aa organism’s potential is
constrained to include only AA or
Aa offspring. In the context of
an aa mate, it has the potential
for Aa or aa offspring, and once again some of this potential
might get actualized. And so forth. But while the mate
constrains the organism’s
potential, the mate is necessary to actualize some of this potential in the form of offspring. In
other words, the genome of the mate simultaneously makes some aspects of the Aa organism’s potentiality possible, and
others impossible. An organism
exists in a state of potentiality with respect to the different offspring
(variants of itself) it could produce with a particular mate. In other words, a
mate constitutes a context for which an organism is in a state of potentiality.
One can get away with ignoring this to the extent that one can assume mating is
random. Note that since a species is delineated according to the capacity of
individuals to mate with one another, speciation can be viewed as the situation
wherein one variant no longer has the potential to create a context for the
other for which its state is a potentiality state with respect to offspring. A
species can be said to be adapted to the extent that its previous states
could have collapsed to different
outcomes in different contexts, and thus to the extent its form reflects the
particular contexts to which it was exposed. Note also that although over time species
become increasingly context dependent, collectively they are becoming more
context independent. (For
virtually any ecological niche there exists some branch of life that can cope with it.)

It is often assumed that if an entity constitutes a unit of
evolution it follows automatically that it constitutes a unit of selection. But
there is no reason evolution need involve selection, except as a special case.
The assumptions of natural selection¾including
coded replication, successive generations, and negligible inheritance of
acquired characteristics¾are
natural and non-problematic in considerations of modern-day prokaryotes and
eukaryotes. However they are unnatural and problematic in considerations of life
prior to genetically mediated protein synthesis. We are so used to thinking in
terms of competition amongst individuals with genomes and fixed lifespans that
it may have warped our ability to think clearly about entities without fixed
lifespans, that replicate without genomes. Their challenge may be not so much to
out-compete neighbors as to enhance ones’ own energetic efficiency enough to
maintain and replicate a ‘proto-metabolism’ (irregardless of how well neighbors
are doing).

In fact the problem with applying natural selection to
autocells is not merely that it would be possible for an autocell to evolve¾undergo
adaptive descent with modification¾without
selection. The problem is more acute: the assumptions that render natural
selection applicable to the description of genetically mediated life do not hold
for autocells. Autocells do not engage in template replication; their
replication proceeds without a code through a self-organized autocatalytic
process. As a result, there is no definitive distinction between dead and alive,
and more importantly, since replication is not code-driven, acquired
characteristics are inherited. A modified version of selection theory or
self-organization would not work because the description of change-of-state
through interaction with an incompletely specified context has a completely
different mathematical structure, i.e.
entails a non-Kolmogorovian probability model. It is possible to ignore this
effect of context if all contexts are equally likely, or if context has a
limited effect on heritability. Context does have a limited effect on
heritability for code-mediated forms of life, but not for uncoded forms of life
such as autocells, which replicate through a self-organized autocatalytic
process. Neither is the addition of self-organization to a theory of evolution
sufficient to complete the picture, because it is restricted to explanation of
change that occurs through interactions amongst parts rather than interaction
with a context.

It is proposed that the evolution of early life occurred by
transformation of lineages through context-driven actualization of potential, or
CAP, rather than competitive exclusion of less fit individuals through natural
selection. Specifically:

·An entity has the
potential to change in
different ways under different contexts.

·Some aspects of
this potentiality are actualized when the entity undergoes a change of state
through interaction with the particular context itencounters.

·The interaction
between entity and context may also change the context, and the constraints and
affordances it offers the entity.

Thus the entity
undergoes another change of state, and so forth, recursively. Different
evolutionary processes vary with respect to the degree of indeterminism due to
context, the degree of context independence, and the degree to which
context-driven change is retained in future lineage(s).

This move constitutes a crucial step toward incorporating
‘relation’ into our theories of living things (Rosen 1991). Almost every entity
changes through this simpler means of contextual interaction, not only physical
entities, but also cultural artifacts (Gabora 2004; Gabora and Aerts 2005) and
ideas as they are being honed in a stream of thought (Gabora 2005). This paper
has argued that natural selection becomes salient in the evolution of a lineage
only after a self-replication code
(e.g. genetic code) has been
established. Only once change accrued through contextual interaction starts to
get wiped out at the end of each generation can selection start to play a
dominant role in evolution. It is somewhat ironic that though the term
‘adaptation’ is most closely associated with biology, biological form is in fact
exceptionally resistant to internalization and retention of context-driven
change. This explains why it has been possible to develop a theory of biological
evolution that all but ignores the problem of incomplete knowledge of context.

Acknowledgements

This paper owes its existence to the cheerful skepticism and
healthy challenges put to me by Jeremy Sherman and Terry Deacon in discussions
of these ideas.

[1]
Dyson (1985) reserves the term ‘replicator’ for coded replicators, and uses the
term ‘reproducer’ for uncoded replicators. However, this terminology is
potentially confusing given that elsewhere (e.g. Szathmáry & Maynard Smith 1997) the term
‘reproducer’ is associated with sexual reproduction. The distinction between
coded and uncoded replicators is related, but not identical to, Szathmáry and
Maynard Smith’s (1997) distinction between modular and processive
replicators.

[2]
Kauffman’s proposal for how life began has been criticized for its strict
mathematical assumptions such as binary sequences (only two amino acids) and
each molecule had the same fixed probability of catalyzing a given reaction
(Joyce 1989; Lifson 1997; Maynard-Smith & Szathmary 1995), though conditions
under which autocatalysis occurs with under looser sets of assumptions have been
delineated (Hordijk & Steel 2004; Mossel & Steel 2005; Steel
2000).

[3]
Since the existence of the genotype was not known at the time the notion was
proposed by Lamarck, this strict interpretation of the concept is surely not
what he had in mind. This is why it is the looser use of the term that is
adopted here; thus any means by which acquired traits are inherited counts as
Lamarckian.

[4]Kolmogorov
(1933) formulated the axiomatic system for classical probability theory. Thus a
classical probability theory is one that satisfies Kolmogorov’s axioms
(i.e. a Kolmogorovian probability theory). It is well known that
the probability calculus of quantum mechanics is non-classical and that it does
not satisfy Kolmogorov’s axioms (Wilce, 2003).

[5]Yet another possibility is that the
nondeterminism is ontological i.e. the universe is intrinsically
nondeterministic at bottom. In this case, it can be shown that the mathematical
structure necessary to model the situation is equivalent to the mathematical
structure needed to model the situation where the nondeterminism arose through
lack of knowledge of the context, i.e. the probability model needed also does
not satisfy Kolmogorov’s axioms. Hence a probability model satisfying the axioms
of Kolmogorov suits only the type of indeterminism with underlying determinism
(Aerts, 1994). Thus, ontological indeterminism can also be described in this
framework.